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Similarity Constrained Latent Support Vector Machine: An Application to Weakly Supervised Action Classification

  • Nataliya Shapovalova
  • Arash Vahdat
  • Kevin Cannons
  • Tian Lan
  • Greg Mori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7578)

Abstract

We present a novel algorithm for weakly supervised action classification in videos. We assume we are given training videos annotated only with action class labels. We learn a model that can classify unseen test videos, as well as localize a region of interest in the video that captures the discriminative essence of the action class. A novel Similarity Constrained Latent Support Vector Machine model is developed to operationalize this goal. This model specifies that videos should be classified correctly, and that the latent regions of interest chosen should be coherent over videos of an action class. The resulting learning problem is challenging, and we show how dual decomposition can be employed to render it tractable. Experimental results demonstrate the efficacy of the method.

Keywords

Latent Variable Action Recognition Latent Region Test Video Training Video 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nataliya Shapovalova
    • 1
  • Arash Vahdat
    • 1
  • Kevin Cannons
    • 1
  • Tian Lan
    • 1
  • Greg Mori
    • 1
  1. 1.School of Computing ScienceSimon Fraser UniversityCanada

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